The present invention relates to a ghost cancelling apparatus and method for a television video signal, and more particularly, to an apparatus and method for automatically canceling a ghost within an input video signal using a transversal filter.
When a television signal is transmitted via a channel, the television signal includes a ghost signal as a result of multi-paths which are formed when the television video signal is reflected by a large object such as a building, etc. Such a ghost signal is received after being delayed in time in comparison with the original signal due to the difference in lengths of the signal paths. When the television signal is reproduced in a receiver, the ghost signal, having a smaller amplitude than that of the original signal, is displayed on a screen as a dark image which is offset from the image of the original signal.
To eradicate the unwanted ghost signal from the received signal, various ghost signal cancelling techniques have been proposed. However, such conventional ghost signal cancelling techniques utilize a common principle, in which the original signal is delayed so as to match the ghost signal in time, and the amplitude of the original signal is attenuated so to match that of the ghost signal. The attenuated and delayed original signal, that is, a ghost correction signal, is then provided to cancel the ghost signal.
A basic step of effectively cancelling a ghost signal within an input video signal, is a step of correctly extracting the existence of the ghost component in the input signal, that is, relative to its magnitude and location. In order to provide this correction step, the television signal transmission station transmits a ghost cancelling reference signal (GCR) a predetermined time before transmitting the video signal, and the receiver compares the received signal distorted during transmission with a signal corresponding to the reference signal in order to detect the location and magnitude of the ghost.
Conventionally, the cross-correlation is performed between the initially received video signal and a reference signal GCR. Using the result of the cross-correlation, the location and magnitude of the ghost are extracted and the filter coefficient is initialized. Thereafter, the filter coefficient is corrected according to the conventional least mean square (LMS) algorithm which is based on the difference between the output y(n) and the reference signal r(n), that is, error signal e(n), thereby obtaining an optimal filter coefficient to cancel the ghost. A system for performing this conventional method is shown in FIG. 1 and includes an error signal detector 3 for providing the error signal e(n), a LMS filter coefficient corrector 4 for receiving the error signal e(n), a transversal filter 2 which receives the output of the corrector 4 and a subtractor 1 for receiving input signal x(n) and the output of filter 2.
However, in the conventional ghost cancelling method, when the input signal includes a spread ghost due to multiple ghosts each having nearly the same delay time as shown in FIG. 2A, the spread ghost is recognized as a single ghost to be processed. Accordingly, it takes a long time to remove the ghost, and further, it is difficult to sufficiently remove the ghost. That is, when the spread ghost exists as shown in FIG. 2A, only that ghost having the maximum peak value is recognized, and therefore the spread ghost is detected as a single ghost as shown in FIG. 2B. As a result, the remainder of the ghost above the threshold value as shown in FIG. 2C, still exists in the output even after the ghost has been removed from the input signal as described above. When such a remaining ghost above the threshold value is supposed to be removed by repeatedly correcting the filter coefficient using an LMS algorithm based on an error signal between the output signal and a reference signal according to the conventional ghost cancelling method, it takes too much time and results in insufficient removal of the ghost.